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This dataset contains meteorological, oceanographic, and sensor-depth data collected near Block Island during the 2016 and 2017 RODEO field seasons to provide environmental context for concurrent acoustic and survey operations. It includes buoy-derived wind and wave time series, in-situ water temperature logger records, and depth (pressure) records from a sensor mounted on the vertical line array.
Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the occupants. As depth sensors are getting cheaper, they offer a viable solution to estimate occupancy accurately in a non-privacy invasive manner. Even though there are publicly available depth datasets, they do not consider placing the sensor in the ceiling looking downwards to estimate occupancy. We deployed four Kinect for XBOX One in four CMU classrooms and conference rooms for a period of four weeks in 2017 and collected over 6 TB of depth data. We annotate this huge dataset by labelling bounding boxes around occupants and release the annotated dataset.
Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the occupants. As depth sensors are getting cheaper, they offer a viable solution to estimate occupancy accurately in a non-privacy invasive manner. Even though there are publicly available depth datasets, they do not consider placing the sensor in the ceiling looking downwards to estimate occupancy. We deployed four Kinect for XBOX One in four CMU classrooms and conference rooms for a period of four weeks in 2017 and collected over 6 TB of depth data. We annotate this huge dataset by labelling bounding boxes around occupants and release the annotated dataset.
Transducer utilizes absorptive properties of water to determine variations in depth without disturbing liquid. Instrument is inexpensive, simple, and small and thus can be used in lieu of direct graduated scale readout or capacitive, ultrasonic, resistive or inducive sensors when these are impractical because of complexity or cost.
Thermal comfort is very important for well-being and productivity of building occupants. It has been shown that body shape is a useful feature to determine thermal comfort of individuals [2]. It is because, the heat dissipation rate of individuals depends on the body surface area. As a result, a tall and skinny person can tolerate higher room temperature than a rounded body shape person [5]. In order to test this hypothesis, we performed a year-long experiment in 2017, where we recruited 77 participants and put each of them in a thermally controlled conference room in CMU for 3 hours and recorded their subjective responses regarding thermal comfort at different temperature ranging from 60°F to 80°F. In addition, we collected depth data of individuals using a vertically mounted Microsoft Kinect for XBOX One at the entrance of the conference room to capture their body shape. We also collected biometric features (e.g., Galvanic Skin Response (GSR), skin temperature) using a Microsoft Health Band worn by the subjects. The resulting dataset provides rich information regarding how different features can be used to infer thermal comfort of the individuals.
The Mars Science Laboratory will enter the Martian atmosphere in 2012 with multiple char depth sensors and in-depth thermocouples in its heatshield. The aerothermal environment experienced by MSL may be computationally recreated using the data from the sensors and a material response program, such as the Fully Implicit Ablation and Thermal (FIAT) response program, through the matching of the char depth and thermocouple predictions of the material response program to the sensor data. A tool, CHanging Inputs from the Environment of FIAT (CHIEF), was developed to iteratively change different environmental conditions such that FIAT predictions match within certain criteria applied to an external data set. The computational environment is changed by iterating on the enthalpy, pressure, or heat transfer coefficient at certain times in the trajectory. CHIEF was initially compared against arc-jet test data from the development of the MSL heatshield and then against simulated sensor data derived from design trajectories for MSL. CHIEF was able to match char depth and in-depth thermocouple temperatures within the bounds placed upon it for these cases. Further refinement of CHIEF to compare multiple time points and assign convergence criteria may improve accuracy.
A gravity irrigation system includes a distribution piping having apertures to distribute water to a field, and a valve located upstream of the distribution piping. The valve limits a pressure of the water being delivered to the distribution piping. The system also includes a sump to receive the water at a lowest elevation of the field, a depth sensor disposed within the sump, and a return pump disposed at least partially within the sump to move the water to an elevated portion of the field. The system also includes a motor to drive the return pump, and a power source coupled to a variable frequency drive that powers the motor and controls a motor speed proportionately to an indication of the depth sensor. The system also includes a transfer piping to bring the water from the return pump to a check valve and to the distribution piping.
The novel contribution of this instrument is to take the first depth images on the moon. A commercial Kinect camera from Microsoft has been ruggedized for a CLPS mission to the Lunar South Pole. The 12-megapixel color camera is combined with a 1-megapixel color time-of-flight (ToF) depth sensor. The solid-state depth sensor/LIDAR provides greater resolution, a wider field of view, pixel binning, and reduced power consumption. The high-resolution data collected from the mission can be used to construct a near-field virtual environment of the lunar surface for scientific applications. The depth camera can provide a 360-degree view of the target area by combining ToF data with RGB imagery and rover turning. The unit was evaluated for space flight compliant materials and parts at NASA Ames. The microphone array, RF shield, front face, and outer body parts were removed to reduce mass. Plastic parts were replaced with vacuum-compatible materials; manufactured cables were added to properly interface with the host rover, built by Lunar Outpost. The environmental testing was performed on the Kinect with Random Vibration/Sine Testing (Fig 1a), per GEVS (NASA’s General Environmental Verification Standard for spaceflight launch survival). Thermal Vacuum testing cycled the instrument between expected hot (+ 85 C) and cold survival temperatures, as well as +50 C and -25 C operational temps (Fig 1b). The instrument functioned nominally at the conclusion of vibration and thermal cycling tests. The Azure Kinect is manifested on the Nova-C lander which flies on the Intuitive Machines mission IM-2, landing at the lunar south pole for a mission duration of 14 days (one lunar daylight cycle). The unit will be integrated to the MAPP (Mobile Autonomous Prospecting Platform) rover at Lunar Outpost. The Azure Kinect with ToF feature will improve the resolution of lunar geology data near the south pole and enable ground-based VR experience of details of the lunar surface (Fig 1c).
An important challenge with Machine Learning (ML) is its transferability; that is, whether a ML model trained on one set of data can be applied to a second set of data without requiring a full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained on data collected for one set of streets can effectively forecast flooding for another set of streets in the same city using TL. The envisioned use case is a city deploying a new flood depth monitoring sensor on a street and using TL to apply a ML model, trained on sensor data from an existing flood depth sensor network, to this new street. Eventually, the new flood depth sensor will have a sufficient dataset for training its own ML model, but TL can be used to fill the gap in time while this new dataset is being generated. This method is explored using a Long Short-Term Memory (LSTM) model trained on data for the flood-prone streets of Norfolk City, Virginia. The data used for training includes environmental time series (rainfall, tide), topographic features (Digital Elevation Model (DEM), Topographic Wetness Index (TWI), Depth To Water (DTW)), and street-scale flood depth time series obtained from a high-fidelity physics-based model, acting as a synthetic street-scale stream depth sensor dataset since actual stream depth sensor data is generally unavailable for most cities. A set of 180 flood-prone streets was used to train a base model, while another set of 180 flood-prone streets was used to re-train that model using different TL strategies. The results show that full-weight re-training proved most effective and minimal re-training of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As target data increased beyond 18 flood events, the benefit of TL diminished relative to training a ML model directly on the local flood events. These findings can assist cities as they implement street-scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.
The Mars 2020 aeroshell's thermal protection system was nearly identical to the Mars Science Laboratory system that successfully completed its mission for the Curiosity rover's landing in 2012. It was predicted that, like Mars Science Laboratory, the Mars 2020 heatshield would experience boundary layer transition and that the thermal protection system would provide sufficient material thickness margins against the aerothermal environments, even after adding radiative heating that was not included for Mars Science Laboratory. The Mars 2020 flight instrumentation suite included heatshield sensors similar to Mars Science Laboratory and new backshell measurements. A full set of surface pressure and in-depth temperature data were collected during atmospheric entry. This paper provides an initial analysis of the flight data and focuses on the reconstructed total surface heat flux inferred from the measured temperatures. Turbulent boundary layer conditions again were observed on the Mars 2020 heatshield, and the temperatures at all eleven heatshield and six backshell thermocouple in-depth sensor locations were well within system capabilities due to favorable entry conditions and conservative uncertainties. New computational fluid dynamics results on the reconstructed entry trajectory are compared to the measured surface pressures and reconstructed total heat fluxes. The predicted heatshield surface pressures at six locations match the data qualitatively and quantitatively well, as expected. Smooth-wall laminar heating predictions prior to boundary layer transition fall above the reconstructed heating on the heatshield. After the observed boundary layer transition time, total heat flux based on algebraic turbulence model calculations generally match the heat flux trends relative to one another. The convective heat flux was predicted to constitute the majority of the total heating. On the backshell, smooth-wall laminar total heat flux predictions generally exceed the reconstructed total heating at six locations. At each of these locations, it is estimated that radiative heating provided almost all of the total heating. The Mars 2020 as-flown aerothermal environments were well below the design levels for all measurement locations due to conservative design assumptions and a stressing design trajectory.
The Mars 2020 aeroshell's thermal protection system was nearly identical to the Mars Science Laboratory system that successfully completed its mission for the Curiosity rover's landing in 2012. It was predicted that, like Mars Science Laboratory, the Mars 2020 heatshield would experience boundary layer transition and that the thermal protection system would provide sufficient material thickness margins against the aerothermal environments, even after adding radiative heating that was not included for Mars Science Laboratory. The Mars 2020 flight instrumentation suite included heatshield sensors similar to Mars Science Laboratory and new backshell measurements. A full set of surface pressure and in-depth temperature data were collected during atmospheric entry. This paper provides an initial analysis of the flight data and focuses on the reconstructed total surface heat flux inferred from the measured temperatures. Turbulent boundary layer conditions again were observed on the Mars 2020 heatshield, and the temperatures at all eleven heatshield and six backshell thermocouple in-depth sensor locations were well within system capabilities due to favorable entry conditions and conservative uncertainties. New computational fluid dynamics results on the reconstructed entry trajectory are compared to the measured surface pressures and reconstructed total heat fluxes. The predicted heatshield surface pressures at six locations match the data qualitatively and quantitatively well, as expected. Smooth-wall laminar heating predictions prior to boundary layer transition fall above the reconstructed heating on the heatshield. After the observed boundary layer transition time, total heat flux based on algebraic turbulence model calculations generally match the heat flux trends relative to one another. The convective heat flux was predicted to constitute the majority of the total heating. On the backshell, smooth-wall laminar total heat flux predictions generally exceed the reconstructed total heating at six locations. At each of these locations, it is estimated that radiative heating provided almost all of the total heating. The Mars 2020 as-flown aerothermal environments were well below the design levels for all measurement locations due to conservative design assumptions and a stressing design trajectory.
Meteorological data are currently being collected at one location at the Teller Mile 47 (TL_MM47) Research Basin Site, Seward Peninsula (N64 58' 36.918", W166 12' 32.67", 67 meters above sea level). The site was installed and initial measurements started in September 2018 and it has operated continuously since then. The meteorological station is co-located with a continuous snow depth sensor and two soil pits for subsurface temperature and moisture measurements. These data are being collected to better understand the energy dynamics above the active layer and permafrost. They complement in-situ snow and soil measurements also at this location. The data could also be used as supporting measurements for other research and modeling activities. There are 35 comma separated value format (*.csv) files provided, where each file contains the full data for an individual parameter (e.g. air temperature at 1.5 meters above the ground surface (teller_m47_air_temperature_150cm_ags_Avg.csv) or soil temperature 20 centimeters below ground surface (teller_m47_dry_soil_pit_temperature_20cmbgs_Avg.csv)) plus the time in Universal Coordinated Time (UTC) and Alaska Standard Time (UTC time minus nine hours). The site was installed and initial measurements started in September 2018. It has been operated continuously since. Primary data gaps are due to battery failure or sensor failure. These data are being collected to better understand the surface energy dynamics above the active layer and permafrost. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy?s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy?s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
Two applications of optimal stochastic filters to navigation systems are described. The first is an air navigation system consisting of an inertial device (INS) and a Loran, plus an altimeter. The second is an application to a system of submarine navigation consisting of an inertial device (SINS) and an Omega plus a depth sensor.
Two sources of H1/3 estimates were available at Platform harvest; a NOAA buoy and CU depth sensor highrate data.
This project will develop an efficient smoothing software for incremental estimation of the relative poses and velocities between multiple, small spacecraft in a formation, and a small, long range depth sensor based on coded aperture imaging that is capable of identifying other spacecraft in the formation. The smoothing algorithm will obtain the maximum a posteriori estimate of the relative poses between the spacecraft by using all available sensor information in the spacecraft formation.This algorithm will be portable between different satellite platforms that possess different sensor suites and computational capabilities, and will be adaptable in the case that one or more satellites in the formation become inoperable. It will obtain a solution that will approach an exact solution, as opposed to one with linearization approximation that is typical of filtering algorithms. Thus, the algorithms developed and demonstrated as part of this program will enhance the applicability of small spacecraft to multi-platform operations, such as precisely aligned constellations and fractionated satellite systems.
We report here on a survey of a lava tube cave by a rover that is instrumented for astrobiology missions. The NASA Ames testbed rover, CaveR, was deployed in Valentine Cave in Lava Beds National Monument (N. CA, USA) during August of 2018. The rover carried an instrument package consisting of Near Infrared and Visible Spectrometer System (NIRVSS) a point spectrometer operating in 1590-3400 nm range, sensitive to H2O and -OH bearing minerals, pyroxenes, and carbonates (Roush, et al 2018); the bore sighted Drill Operations Camera (DOC), a monochrome imager illuminated by LEDs at 410, 540, 640, 740, 905 and 940 nm; a Realsense ™ depth sensor system for 3D model generation; and a high resolution DSLR stereo camera. The payload was mounted on a tiltable instrument platform attached to the left side of the rover. The rover was driven manually in the cave by field operators, following instructions from a remote science operations team, and simulating a mission concept with science-guided autonomy. A simulated mission took place for 3 days with a team of 3 scientists selecting targets and interpreting data from the payload. To begin the mission, the rover drove along one wall of the cave imaging continuously with the Realsense in 20 m cave segments, three total. At the start of each day, the images from a 20m segment and a panorama stitched from them were provided to the science team to examine. The science team used these data to prioritize specific points along the cave wall for the collection of NIRVSS, DOC, and DSLR data. The objective of the data collection was to identify and study putative biological and mineralogical features in the cave. The data were delivered in xGDS, a customized mapping, planning, and data base management software developed at NASA Ames (Lee, et al 2013). Once the targets for further observations were selected, a plan for collecting the observations (positions in the cave and pointing for each requested observation) was constructed using xGDS and delivered to a rover team to execute the science data collection plan. Acquired data were delivered back to the science team for analysis. Preliminary results from the experiment illustrate the utility of the system (rover plus payload) to study the cave geology and mineralogy and its potential for identifying biomineral features.
Occupant thermal comfort may be inferred and improved using body shape information. Height, weight, and shoulder circumference of an occupant of a room may be obtained using a depth sensor. A model may be utilized that is trained on a dataset including information reflecting of occupant comfort within the room versus temperature, the model receiving, as inputs, the height, the weight, and the shoulder circumference of the occupant and environmental information and outputting a comfort class. A temperature set-point for is identified which the room occupant is identified by the model as having the comfort class being indicative of user comfort. Heating, ventilation, and air conditioning (HVAC) controls are adjusted for the room to the identified temperature set-point.
We present an X-ray characterization of a fully depleted, 725 $μ$m thick p-channel SiSeRO CCD. Measurements with a $^{55}$Fe source yield an energy resolution of $54 \pm 0.9$ eV ($14.6 \pm 0.25 e^{-}$) at 5.9 keV for single-pixel events, demonstrating that the SiSeRO amplifier preserves the intrinsic charge resolution of the CCD under multi-sample non-destructive readout. Characterization with a $^{241}$Am source extends the response to higher-energy photons, with reconstructed spectral features observed between 9-26 keV and the 59.5 keV $γ$ emission. These measurements, together with a muon-derived diffusion calibration, show that charge transport and diffusion are consistent with interactions spanning the full sensor depth. These results demonstrate that the SiSeRO CCD simultaneously achieves sub-electron noise performance and efficient charge collection in a thick, fully depleted silicon detector. This combination enables X-ray spectroscopy across a broad energy range while maintaining sensitivity to faint signals.